1 citations · 2 across the 8 of their papers we have counts for
11 papers
MEG-Mamba: A Scalable State-Space Foundation Model for Magnetoencephalography
Chetan Gohil, SungJun Cho, Oiwi Parker Jones +1
Magnetoencephalography (MEG) is an imaging technique that offers a non-invasive, millisecond-resolution view of human brain activity. The increasing availability of MEG data presen…
The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100
Francesco Mantegna, Gereon Elvers, Dulhan Jayalath +18
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to progress from foundational tasks…
LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale
Francesco Mantegna, Dulhan Jayalath, Gereon Elvers +12
We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. LibriBrain100 more than doubles the…
A Systematic Evaluation of Sample-Level Tokenization Strategies for MEG Foundation Models
SungJun Cho, Chetan Gohil, Rukuang Huang +2
Recent success in natural language processing has motivated growing interest in large-scale foundation models for neuroimaging data. Such models often require discretization of con…
MEG-GPT: A transformer-based foundation model for magnetoencephalography data
Rukuang Huang, Sungjun Cho, Chetan Gohil +2
Modelling the complex spatiotemporal patterns of large-scale brain dynamics is crucial for neuroscience, but traditional methods fail to capture the rich structure in modalities su…
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau, Miran Özdogan, Gereon Elvers +15
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising applications is the restoration of communicat…
LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
Miran Özdogan, Gilad Landau, Gereon Elvers +5
LibriBrain represents the largest single-subject MEG dataset to date for speech decoding, with over 50 hours of recordings -- 5 larger than the next comparable dataset and…
osl-ephys: A Python toolbox for the analysis of electrophysiology data
Mats W. J. van Es, Chetan Gohil, Andrew J. Quinn +1
We describe OHBA Software Library for the analysis of electrophysiological data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique an…
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
Dulhan Jayalath, Gilad Landau, Brendan Shillingford +2
The past few years have seen remarkable progress in the decoding of speech from brain activity, primarily driven by large single-subject datasets. However, due to individual variat…
GPT2MEG: Quantizing MEG for Autoregressive Generation
Richard Csaky, Mats W. J. van Es, Oiwi Parker Jones +1
Foundation models trained with self-supervised objectives are increasingly applied to brain recordings, but autoregressive generation of realistic multichannel neural time series r…
The Gaussian-Linear Hidden Markov model: a Python package
Diego Vidaurre, Laura Masaracchia, Nick Y. Larsen +4
We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework wh…